TKES: A Novel System for Extracting Trendy Keywords from Online News Sites

As the Smart city trend especially artificial intelligence, data science, and the internet of things has attracted lots of attention, many researchers have created various smart applications for improving people's life quality. As it is very essential to automatically collect and exploit information in the era of industry 4.0, a variety of models have been proposed for storage problem solving and efficient data mining. In this paper, we present our proposed system, Trendy Keyword Extraction System (TKES), which is designed for extracting trendy keywords from text streams. The system also supports storing, analyzing, and visualizing documents coming from text streams. The system first automatically collects daily articles, then it ranks the importance of keywords by calculating keywords' frequency of existence in order to find trendy keywords by using the Burst Detection Algorithm which is proposed in this paper based on the idea of Kleinberg. This method is used for detecting bursts. A burst is defined as a period of time when a keyword is continuously and unusually popular over the text stream and the identification of bursts is known as burst detection procedure. The results from user requests could be displayed visually. Furthermore, we create a method in order to find a trendy keyword set which is defined as a set of keywords that belong to the same burst. This work also describes the datasets used for our experiments, processing speed tests of our two proposed algorithms.